Says whether the targets are in the top `K`

predictions.

tf.compat.v2.math.in_top_k( targets, predictions, k, name=None )

This outputs a `batch_size`

bool array, an entry `out[i]`

is `true`

if the prediction for the target class is finite (not inf, -inf, or nan) and among the top `k`

predictions among all predictions for example `i`

. Note that the behavior of `InTopK`

differs from the `TopK`

op in its handling of ties; if multiple classes have the same prediction value and straddle the top-`k`

boundary, all of those classes are considered to be in the top `k`

.

More formally, let

\(predictions_i\) be the predictions for all classes for example `i`

, \(targets_i\) be the target class for example `i`

, \(out_i\) be the output for example `i`

,

$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$

Args | |
---|---|

`predictions` | A `Tensor` of type `float32` . A `batch_size` x `classes` tensor. |

`targets` | A `Tensor` . Must be one of the following types: `int32` , `int64` . A `batch_size` vector of class ids. |

`k` | An `int` . Number of top elements to look at for computing precision. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` of type `bool` . Computed Precision at `k` as a `bool Tensor` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/math/in_top_k